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    Collect Splunk Attack Analyzer logs

    Supported in:

    This document explains how to ingest Splunk Attack Analyzer logs to Google Security Operations using Google Cloud Storage V2.

    Splunk Attack Analyzer (formerly TwinWave) is an automated threat analysis platform that detects phishing and malware through behavioral analysis. It provides completed job results and normalized forensics data through a REST API.

    Before you begin

    Make sure you have the following prerequisites:

    • A Google SecOps instance
    • A GCP project with Cloud Storage API enabled
    • Permissions to create and manage GCS buckets
    • Permissions to manage IAM policies on GCS buckets
    • Permissions to create Cloud Run services, Pub/Sub topics, and Cloud Scheduler jobs
    • Privileged access to Splunk Attack Analyzer with API key generation permissions

    Create Google Cloud Storage bucket

    1. Go to the Google Cloud Console .
    2. Select your project or create a new one.
    3. In the navigation menu, go to Cloud Storage > Buckets.
    4. Click Create bucket.

    5. Provide the following configuration details:

      Setting Value
      Name your bucket Enter a globally unique name (for example, splunk-attack-analyzer-logs )
      Location type Choose based on your needs (Region, Dual-region, Multi-region)
      Location Select the location (for example, us-central1 )
      Storage class Standard (recommended for frequently accessed logs)
      Access control Uniform (recommended)
      Protection tools Optional: Enable object versioning or retention policy

    6. Click Create.

    Collect Splunk Attack Analyzer API credentials

    Generate API key

    1. Log in to Splunk Attack Analyzer .
    2. Select your usernamein the top-right corner, then select API Keys.
    3. Click + New Key.
    4. Enter a descriptive name for the key (for example, Google Security Operations Integration ).
    5. Click Create.

    6. Copy and save the API secretdisplayed in the modal in a secure location.

    Verify permissions

    To verify the API key has the required access:

    1. Log in to Splunk Attack Analyzer .
    2. Select your usernamein the top-right corner, then select API Keys.

    3. Verify the API key is listed and active.

    Test API access

    • Test your credentials before proceeding with the integration:

        # Replace with your actual API key 
 API_KEY 
 = 
 "your-api-key" 
 # Test API access - list completed jobs 
curl  
-v  
-H  
 "Authorization: Bearer 
 ${ 
 API_KEY 
 } 
 " 
  
 \ 
  
 "https://app.twinwave.io/api/v1/jobs?done=true&limit=1"

    The Cloud Run function needs a service account with permissions to write to GCS bucket and be invoked by Pub/Sub.

    1. In the GCP Console, go to IAM & Admin > Service Accounts.
    2. Click Create Service Account.
    3. Provide the following configuration details:
      • Service account name: Enter saa-collector-sa .
      • Service account description: Enter Service account for Cloud Run function to collect Splunk Attack Analyzer logs .
    4. Click Create and Continue.
    5. In the Grant this service account access to projectsection, add the following roles:
      1. Click Select a role.
      2. Search for and select Storage Object Admin.
      3. Click + Add another role.
      4. Search for and select Cloud Run Invoker.
      5. Click + Add another role.
      6. Search for and select Cloud Functions Invoker.
    6. Click Continue.
    7. Click Done.

    These roles are required for:

    • Storage Object Admin: Write logs to GCS bucket and manage state files
    • Cloud Run Invoker: Allow Pub/Sub to invoke the function
    • Cloud Functions Invoker: Allow function invocation

    Grant IAM permissions on GCS bucket

    Grant the service account write permissions on the GCS bucket:

    1. Go to Cloud Storage > Buckets.
    2. Click your bucket name (for example, splunk-attack-analyzer-logs ).
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Enter the service account email (for example, saa-collector-sa@your-project.iam.gserviceaccount.com ).
      • Assign roles: Select Storage Object Admin.
    6. Click Save.

    Create Pub/Sub topic

    Create a Pub/Sub topic that Cloud Scheduler will publish to and the Cloud Run function will subscribe to.

    1. In the GCP Console, go to Pub/Sub > Topics.
    2. Click Create topic.
    3. Provide the following configuration details:
      • Topic ID: Enter saa-trigger .
      • Leave other settings as default.
    4. Click Create.

    Create Cloud Run function to collect logs

    The Cloud Run function will be triggered by Pub/Sub messages from Cloud Scheduler to fetch logs from Splunk Attack Analyzer API and write them to GCS.

    1. In the GCP Console, go to Cloud Run.
    2. Click Create service.
    3. Select Function(use an inline editor to create a function).

    4. In the Configuresection, provide the following configuration details:

      Setting Value
      Service name saa-collector
      Region Select region matching your GCS bucket (for example, us-central1 )
      Runtime Select Python 3.12or later

    5. In the Trigger (optional)section:

      1. Click + Add trigger.
      2. Select Cloud Pub/Sub.
      3. In Select a Cloud Pub/Sub topic, choose the topic saa-trigger .
      4. Click Save.

    6. In the Authenticationsection:

      1. Select Require authentication.
      2. Check Identity and Access Management (IAM).

    7. Scroll down and expand Containers, Networking, Security.

    8. Go to the Securitytab:

      • Service account: Select the service account saa-collector-sa .

    9. Go to the Containerstab:

      1. Click Variables & Secrets.
      2. Click + Add variablefor each environment variable:
      Variable Name Example Value Description
      GCS_BUCKET
      		splunk-attack-analyzer-logs GCS bucket name
      GCS_PREFIX
      		saa Prefix for log files
      STATE_KEY
      		saa/state.json State file path
      API_KEY
      		your-api-key Splunk Attack Analyzer API key
      API_BASE
      		https://app.twinwave.io API base URL
      MAX_RECORDS
      		5000 Max records per run
      PAGE_SIZE
      		100 Records per page
      LOOKBACK_HOURS
      		24 Initial lookback period

    10. Scroll down in the Variables & Secretstab to Requests:

      • Request timeout: Enter 600 seconds (10 minutes).

    11. Go to the Settingstab in Containers:

      • In the Resourcessection:
        • Memory: Select 512 MiBor higher.
        • CPU: Select 1.

    12. In the Revision scalingsection:

      • Minimum number of instances: Enter 0 .
      • Maximum number of instances: Enter 100 (or adjust based on expected load).

    13. Click Create.

    14. Wait for the service to be created (1-2 minutes).

    15. After the service is created, the inline code editorwill open automatically.

    Add function code

    1. Enter mainin Function entry point.

    2. In the inline code editor, create two files:

      • First file: main.py:

          import 
  
 functions_framework 
 from 
  
 google.cloud 
  
 import 
  storage 
 
 import 
  
 json 
 import 
  
 os 
 import 
  
 urllib3 
 from 
  
 datetime 
  
 import 
 datetime 
 , 
 timezone 
 , 
 timedelta 
 import 
  
 time 
 # Initialize HTTP client with timeouts 
 http 
 = 
 urllib3 
 . 
 PoolManager 
 ( 
 timeout 
 = 
 urllib3 
 . 
 Timeout 
 ( 
 connect 
 = 
 5.0 
 , 
 read 
 = 
 30.0 
 ), 
 retries 
 = 
 False 
 , 
 ) 
 # Initialize Storage client 
 storage_client 
 = 
  storage 
 
 . 
  Client 
 
 () 
 # Environment variables 
 GCS_BUCKET 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_BUCKET' 
 ) 
 GCS_PREFIX 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'GCS_PREFIX' 
 , 
 'saa' 
 ) 
 STATE_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'STATE_KEY' 
 , 
 'saa/state.json' 
 ) 
 API_KEY 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'API_KEY' 
 , 
 '' 
 ) 
 API_BASE 
 = 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'API_BASE' 
 , 
 'https://app.twinwave.io' 
 ) 
 . 
 rstrip 
 ( 
 '/' 
 ) 
 MAX_RECORDS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'MAX_RECORDS' 
 , 
 '5000' 
 )) 
 PAGE_SIZE 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'PAGE_SIZE' 
 , 
 '100' 
 )) 
 LOOKBACK_HOURS 
 = 
 int 
 ( 
 os 
 . 
 environ 
 . 
 get 
 ( 
 'LOOKBACK_HOURS' 
 , 
 '24' 
 )) 
 def 
  
 parse_datetime 
 ( 
 value 
 : 
 str 
 ) 
 - 
> datetime 
 : 
  
 """Parse ISO datetime string to datetime object.""" 
 if 
 value 
 . 
 endswith 
 ( 
 "Z" 
 ): 
 value 
 = 
 value 
 [: 
 - 
 1 
 ] 
 + 
 "+00:00" 
 return 
 datetime 
 . 
 fromisoformat 
 ( 
 value 
 ) 
 @functions_framework 
 . 
 cloud_event 
 def 
  
 main 
 ( 
 cloud_event 
 ): 
  
 """ 
 Cloud Run function triggered by Pub/Sub to fetch Splunk Attack Analyzer logs and write to GCS. 
 Args: 
 cloud_event: CloudEvent object containing Pub/Sub message 
 """ 
 if 
 not 
 all 
 ([ 
 GCS_BUCKET 
 , 
 API_KEY 
 ]): 
 print 
 ( 
 'Error: Missing required environment variables' 
 ) 
 return 
 try 
 : 
 bucket 
 = 
 storage_client 
 . 
  bucket 
 
 ( 
 GCS_BUCKET 
 ) 
 # Load state 
 state 
 = 
 load_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 ) 
 # Determine time window 
 now 
 = 
 datetime 
 . 
 now 
 ( 
 timezone 
 . 
 utc 
 ) 
 last_time 
 = 
 None 
 if 
 isinstance 
 ( 
 state 
 , 
 dict 
 ) 
 and 
  state 
 
 . 
 get 
 ( 
 "last_event_time" 
 ): 
 try 
 : 
 last_time 
 = 
 parse_datetime 
 ( 
 state 
 [ 
 "last_event_time" 
 ]) 
 last_time 
 = 
 last_time 
 - 
 timedelta 
 ( 
 minutes 
 = 
 2 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse last_event_time: 
 { 
 e 
 } 
 " 
 ) 
 if 
 last_time 
 is 
 None 
 : 
 last_time 
 = 
 now 
 - 
 timedelta 
 ( 
 hours 
 = 
 LOOKBACK_HOURS 
 ) 
 print 
 ( 
 f 
 "Fetching jobs from 
 { 
 last_time 
 . 
 isoformat 
 () 
 } 
 to 
 { 
 now 
 . 
 isoformat 
 () 
 } 
 " 
 ) 
 # Fetch completed jobs 
 jobs 
 , 
 newest_event_time 
 = 
 fetch_jobs 
 ( 
 start_time 
 = 
 last_time 
 , 
 end_time 
 = 
 now 
 , 
 page_size 
 = 
 PAGE_SIZE 
 , 
 max_records 
 = 
 MAX_RECORDS 
 , 
 ) 
 if 
 not 
 jobs 
 : 
 print 
 ( 
 "No new completed jobs found." 
 ) 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 now 
 . 
 isoformat 
 ()) 
 return 
 # Fetch forensics for each job 
 all_records 
 = 
 [] 
 for 
 job 
 in 
 jobs 
 : 
 job_id 
 = 
 job 
 . 
 get 
 ( 
 'id' 
 , 
 '' 
 ) 
 if 
 not 
 job_id 
 : 
 continue 
 forensics 
 = 
 fetch_forensics 
 ( 
 job_id 
 ) 
 if 
 forensics 
 : 
 # Combine job metadata with forensics 
 record 
 = 
 { 
 'job' 
 : 
 job 
 , 
 'forensics' 
 : 
 forensics 
 } 
 all_records 
 . 
 append 
 ( 
 record 
 ) 
 if 
 not 
 all_records 
 : 
 print 
 ( 
 "No forensics data retrieved." 
 ) 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 now 
 . 
 isoformat 
 ()) 
 return 
 # Write to GCS as NDJSON 
 timestamp 
 = 
 now 
 . 
 strftime 
 ( 
 '%Y%m 
 %d 
 _%H%M%S' 
 ) 
 object_key 
 = 
 f 
 " 
 { 
 GCS_PREFIX 
 } 
 /logs_ 
 { 
 timestamp 
 } 
 .ndjson" 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 object_key 
 ) 
 ndjson 
 = 
 ' 
 \n 
 ' 
 . 
 join 
 ([ 
 json 
 . 
 dumps 
 ( 
 record 
 , 
 ensure_ascii 
 = 
 False 
 ) 
 for 
 record 
 in 
 all_records 
 ]) 
 + 
 ' 
 \n 
 ' 
 blob 
 . 
  upload_from_string 
 
 ( 
 ndjson 
 , 
 content_type 
 = 
 'application/x-ndjson' 
 ) 
 print 
 ( 
 f 
 "Wrote 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 records to gs:// 
 { 
 GCS_BUCKET 
 } 
 / 
 { 
 object_key 
 } 
 " 
 ) 
 if 
 newest_event_time 
 : 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 newest_event_time 
 ) 
 else 
 : 
 save_state 
 ( 
 bucket 
 , 
 STATE_KEY 
 , 
 now 
 . 
 isoformat 
 ()) 
 print 
 ( 
 f 
 "Successfully processed 
 { 
 len 
 ( 
 all_records 
 ) 
 } 
 records" 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 'Error processing logs: 
 { 
 str 
 ( 
 e 
 ) 
 } 
 ' 
 ) 
 raise 
 def 
  
 load_state 
 ( 
 bucket 
 , 
 key 
 ): 
  
 """Load state from GCS.""" 
 try 
 : 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 if 
 blob 
 . 
 exists 
 (): 
 state_data 
 = 
 blob 
 . 
  download_as_text 
 
 () 
 return 
 json 
 . 
 loads 
 ( 
 state_data 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not load state: 
 { 
 e 
 } 
 " 
 ) 
 return 
 {} 
 def 
  
 save_state 
 ( 
 bucket 
 , 
 key 
 , 
 last_event_time_iso 
 : 
 str 
 ): 
  
 """Save the last event timestamp to GCS state file.""" 
 try 
 : 
 state 
 = 
 { 
 'last_event_time' 
 : 
 last_event_time_iso 
 } 
 blob 
 = 
 bucket 
 . 
 blob 
 ( 
 key 
 ) 
 blob 
 . 
  upload_from_string 
 
 ( 
 json 
 . 
 dumps 
 ( 
 state 
 , 
 indent 
 = 
 2 
 ), 
 content_type 
 = 
 'application/json' 
 ) 
 print 
 ( 
 f 
 "Saved state: last_event_time= 
 { 
 last_event_time_iso 
 } 
 " 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not save state: 
 { 
 e 
 } 
 " 
 ) 
 def 
  
 fetch_jobs 
 ( 
 start_time 
 : 
 datetime 
 , 
 end_time 
 : 
 datetime 
 , 
 page_size 
 : 
 int 
 , 
 max_records 
 : 
 int 
 ): 
  
 """ 
 Fetch completed jobs from Splunk Attack Analyzer API with pagination and rate limiting. 
 Args: 
 start_time: Start time for job query 
 end_time: End time for job query 
 page_size: Number of records per page 
 max_records: Maximum total records to fetch 
 Returns: 
 Tuple of (jobs list, newest_event_time ISO string) 
 """ 
 endpoint 
 = 
 f 
 " 
 { 
 API_BASE 
 } 
 /api/v1/jobs" 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 API_KEY 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 'GoogleSecOps-SAACollector/1.0' 
 } 
 records 
 = 
 [] 
 newest_time 
 = 
 None 
 page_num 
 = 
 0 
 backoff 
 = 
 1.0 
 offset 
 = 
 0 
 while 
 True 
 : 
 page_num 
 += 
 1 
 if 
 len 
 ( 
 records 
 ) 
> = 
 max_records 
 : 
 print 
 ( 
 f 
 "Reached max_records limit ( 
 { 
 max_records 
 } 
 )" 
 ) 
 break 
 current_limit 
 = 
 min 
 ( 
 page_size 
 , 
 max_records 
 - 
 len 
 ( 
 records 
 )) 
 url 
 = 
 f 
 " 
 { 
 endpoint 
 } 
 ?done=true&limit= 
 { 
 current_limit 
 } 
& offset= 
 { 
 offset 
 } 
 " 
 try 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 url 
 , 
 headers 
 = 
 headers 
 ) 
 if 
 response 
 . 
 status 
 == 
 429 
 : 
 retry_after 
 = 
 int 
 ( 
 response 
 . 
 headers 
 . 
 get 
 ( 
 'Retry-After' 
 , 
 str 
 ( 
 int 
 ( 
 backoff 
 )))) 
 print 
 ( 
 f 
 "Rate limited (429). Retrying after 
 { 
 retry_after 
 } 
 s..." 
 ) 
 time 
 . 
 sleep 
 ( 
 retry_after 
 ) 
 backoff 
 = 
 min 
 ( 
 backoff 
 * 
 2 
 , 
 30.0 
 ) 
 continue 
 backoff 
 = 
 1.0 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 print 
 ( 
 f 
 "HTTP Error: 
 { 
 response 
 . 
 status 
 } 
 " 
 ) 
 response_text 
 = 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 ) 
 print 
 ( 
 f 
 "Response body: 
 { 
 response_text 
 } 
 " 
 ) 
 return 
 [], 
 None 
 data 
 = 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 page_results 
 = 
 data 
 . 
 get 
 ( 
 'jobs' 
 , 
 []) 
 if 
 not 
 page_results 
 : 
 print 
 ( 
 f 
 "No more results (empty page)" 
 ) 
 break 
 # Filter by time window 
 filtered 
 = 
 [] 
 for 
 job 
 in 
 page_results 
 : 
 created 
 = 
 job 
 . 
 get 
 ( 
 'created_at' 
 , 
 '' 
 ) 
 if 
 created 
 : 
 try 
 : 
 job_time 
 = 
 parse_datetime 
 ( 
 created 
 ) 
 if 
 start_time 
< = 
 job_time 
< = 
 end_time 
 : 
 filtered 
 . 
 append 
 ( 
 job 
 ) 
 if 
 newest_time 
 is 
 None 
 or 
 job_time 
> parse_datetime 
 ( 
 newest_time 
 ): 
 newest_time 
 = 
 created 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Could not parse job time: 
 { 
 e 
 } 
 " 
 ) 
 filtered 
 . 
 append 
 ( 
 job 
 ) 
 print 
 ( 
 f 
 "Page 
 { 
 page_num 
 } 
 : Retrieved 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 jobs, 
 { 
 len 
 ( 
 filtered 
 ) 
 } 
 in time window" 
 ) 
 records 
 . 
 extend 
 ( 
 filtered 
 ) 
 if 
 len 
 ( 
 page_results 
 ) 
< page_size 
 : 
 print 
 ( 
 f 
 "Reached last page (size= 
 { 
 len 
 ( 
 page_results 
 ) 
 } 
 < limit= 
 { 
 page_size 
 } 
 )" 
 ) 
 break 
 offset 
 += 
 len 
 ( 
 page_results 
 ) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Error fetching jobs: 
 { 
 e 
 } 
 " 
 ) 
 return 
 [], 
 None 
 print 
 ( 
 f 
 "Retrieved 
 { 
 len 
 ( 
 records 
 ) 
 } 
 total jobs from 
 { 
 page_num 
 } 
 pages" 
 ) 
 return 
 records 
 , 
 newest_time 
 def 
  
 fetch_forensics 
 ( 
 job_id 
 : 
 str 
 ): 
  
 """ 
 Fetch normalized forensics for a specific job. 
 Args: 
 job_id: The job ID 
 Returns: 
 Forensics data dict or None 
 """ 
 endpoint 
 = 
 f 
 " 
 { 
 API_BASE 
 } 
 /api/v1/jobs/ 
 { 
 job_id 
 } 
 /normalizedforensics" 
 headers 
 = 
 { 
 'Authorization' 
 : 
 f 
 'Bearer 
 { 
 API_KEY 
 } 
 ' 
 , 
 'Accept' 
 : 
 'application/json' 
 , 
 'User-Agent' 
 : 
 'GoogleSecOps-SAACollector/1.0' 
 } 
 backoff 
 = 
 1.0 
 max_retries 
 = 
 3 
 for 
 attempt 
 in 
 range 
 ( 
 max_retries 
 ): 
 try 
 : 
 response 
 = 
 http 
 . 
 request 
 ( 
 'GET' 
 , 
 endpoint 
 , 
 headers 
 = 
 headers 
 ) 
 if 
 response 
 . 
 status 
 == 
 429 
 : 
 retry_after 
 = 
 int 
 ( 
 response 
 . 
 headers 
 . 
 get 
 ( 
 'Retry-After' 
 , 
 str 
 ( 
 int 
 ( 
 backoff 
 )))) 
 print 
 ( 
 f 
 "Rate limited (429) on forensics for job 
 { 
 job_id 
 } 
 . Retrying after 
 { 
 retry_after 
 } 
 s..." 
 ) 
 time 
 . 
 sleep 
 ( 
 retry_after 
 ) 
 backoff 
 = 
 min 
 ( 
 backoff 
 * 
 2 
 , 
 30.0 
 ) 
 continue 
 if 
 response 
 . 
 status 
 != 
 200 
 : 
 print 
 ( 
 f 
 "Warning: Could not fetch forensics for job 
 { 
 job_id 
 } 
 : HTTP 
 { 
 response 
 . 
 status 
 } 
 " 
 ) 
 return 
 None 
 return 
 json 
 . 
 loads 
 ( 
 response 
 . 
 data 
 . 
 decode 
 ( 
 'utf-8' 
 )) 
 except 
 Exception 
 as 
 e 
 : 
 print 
 ( 
 f 
 "Warning: Error fetching forensics for job 
 { 
 job_id 
 } 
 : 
 { 
 e 
 } 
 " 
 ) 
 if 
 attempt 
< max_retries 
 - 
 1 
 : 
 time 
 . 
 sleep 
 ( 
 backoff 
 ) 
 backoff 
 = 
 min 
 ( 
 backoff 
 * 
 2 
 , 
 30.0 
 ) 
 continue 
 return 
 None 
 return 
 None

      • Second file: requirements.txt:

         functions-framework==3.*
google-cloud-storage==2.*
urllib3>=2.0.0

    3. Click Deployto save and deploy the function.

    4. Wait for deployment to complete (2-3 minutes).

    Create Cloud Scheduler job

    Cloud Scheduler will publish messages to the Pub/Sub topic at regular intervals, triggering the Cloud Run function.

    1. In the GCP Console, go to Cloud Scheduler.
    2. Click Create Job.

    3. Provide the following configuration details:

      Setting Value
      Name saa-collector-hourly
      Region Select same region as Cloud Run function
      Frequency 0 * * * * (every hour, on the hour)
      Timezone Select timezone (UTC recommended)
      Target type Pub/Sub
      Topic Select the topic saa-trigger
      Message body {} (empty JSON object)

    4. Click Create.

    Schedule frequency options

    Choose frequency based on log volume and latency requirements:

    Frequency Cron Expression Use Case
    Every 5 minutes
    		 */5 * * * * High-volume, low-latency
    Every 15 minutes
    		 */15 * * * * Medium volume
    Every hour
    		 0 * * * * Standard (recommended)
    Every 6 hours
    		 0 */6 * * * Low volume, batch processing
    Daily
    		 0 0 * * * Historical data collection

    Test the integration

    1. In the Cloud Schedulerconsole, find your job ( saa-collector-hourly ).
    2. Click Force runto trigger manually.
    3. Wait a few seconds and go to Cloud Run > Services > saa-collector > Logs.

    4. Verify the function executed successfully. Look for:

       Fetching jobs from YYYY-MM-DDTHH:MM:SS+00:00 to YYYY-MM-DDTHH:MM:SS+00:00
Page 1: Retrieved X jobs, Y in time window
Wrote Z records to gs://splunk-attack-analyzer-logs/saa/logs_YYYYMMDD_HHMMSS.ndjson
Successfully processed Z records

    5. Check the GCS bucket ( splunk-attack-analyzer-logs ) to confirm logs were written.

    If you see errors in the logs:

    • HTTP 401: Check API key in environment variables
    • HTTP 403: Verify API key has required permissions
    • HTTP 429: Rate limiting - function will automatically retry with backoff
    • Missing environment variables: Check all required variables are set

    Configure a feed in Google SecOps to ingest Splunk Attack Analyzer logs

    1. Go to SIEM Settings > Feeds.
    2. Click Add New Feed.
    3. Click Configure a single feed.
    4. In the Feed namefield, enter a name for the feed (for example, Splunk Attack Analyzer Logs ).
    5. Select Google Cloud Storage V2as the Source type.
    6. Select Splunk Attack Analyzeras the Log type.

    7. Click Get Service Account. A unique service account email will be displayed, for example:

       chronicle-12345678@chronicle-gcp-prod.iam.gserviceaccount.com

    8. Copy this email address. You will use it in the next step.

    9. Click Next.

    10. Specify values for the following input parameters:

      • Storage bucket URL: Enter the GCS bucket URI with the prefix path:

         gs://splunk-attack-analyzer-logs/saa/
        • Replace:
          • splunk-attack-analyzer-logs : Your GCS bucket name.
          • saa : Optional prefix/folder path where logs are stored (leave empty for root).

      • Source deletion option: Select the deletion option according to your preference:

        • Never: Never deletes any files after transfers (recommended for testing).
        • Delete transferred files: Deletes files after successful transfer.

        • Delete transferred files and empty directories: Deletes files and empty directories after successful transfer.

      • Maximum File Age: Include files modified in the last number of days (default is 180 days).

      • Asset namespace: The asset namespace .

      • Ingestion labels: The label to be applied to the events from this feed.

    11. Click Next.

    12. Review your new feed configuration in the Finalizescreen, and then click Submit.

    The Google SecOps service account needs Storage Object Viewerrole on your GCS bucket.

    1. Go to Cloud Storage > Buckets.
    2. Click your bucket name ( splunk-attack-analyzer-logs ).
    3. Go to the Permissionstab.
    4. Click Grant access.
    5. Provide the following configuration details:
      • Add principals: Paste the Google SecOps service account email.
      • Assign roles: Select Storage Object Viewer.

    6. Click Save.

    UDM mapping table

    Log Field UDM Mapping Logic
    when
    		 metadata.event_timestamp When the event occurred
    deviceName
    		 principal.hostname Hostname of the principal
    messageid
    		 metadata.id Unique identifier for the event
    action
    		 security_result.action Action taken by the security product
    protocol
    		 network.ip_protocol IP protocol
    srcAddr
    		 principal.ip IP address of the principal
    srcPort
    		 principal.port Port number of the principal
    dstAddr
    		 target.ip IP address of the target
    dstPort
    		 target.port Port number of the target
    		metadata.event_type Type of event
    		metadata.product_name Product name
    		metadata.vendor_name Vendor/company name

    Need more help? Get answers from Community members and Google SecOps professionals.

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